The University of Queensland Contact magazine has done a story on our documentary Toward Singularity. Big thanks to Harriet Dempsey-Jones for the write up and Editor Michael Jones. This site uses Akismet to reduce spam. Learn how your comment data is processed. Independent science writing and journalism, that is free to the public, is an important part of social change, especially in this age of rapidly developing technologies.

A radar-like tool that can track movements throughout the day without using an invasive camera could be used in care homes to alert staff if a resident falls over. A team from MIT's Computer Science and Artificial Intelligence lab say the goal was to create a device that can track movement without physical contact. A sensor on the wall sends out a continuous low power radio wave and when the waves hits a person or object they bounce back to the sensor. The sensor can then analyse the wave for information about the environment and tell exactly what the person in the room is doing - continuously throughout the day. Researchers say this could be used to create a diary of movement and alert staff if a resident in a care home falls over or doesn't move for a long period of time.

… Asia Pacific has released a whitepaper on the emerging use of artificial intelligence & machine learning technology in the mineral processing sector.

Researchers with the University of California Berkeley and Google Research have published a new paper detailing an AI that can remove unwanted shadows from images. The algorithm focuses on two different types of shadows -- ones from external objects and ones naturally resulting from facial features -- and works to either remove or soften them in order to maintain a natural appearance. Whereas professional images are often taken in a studio with proper lighting, the average snapshot of a person is taken'in the wild' where lighting conditions may be harsh, causing dark shadows that obscure parts of the subject's face while other parts are covered with excessive highlights. The newly developed AI is designed to address this problem by targeting those unwanted shadows and highlights, removing and softening them until a clearer subject remains. The researchers say their tool works in a'realistic and controllable way,' and it could prove useful for more than just images captured in casual settings.

For a very long time, Artificial Intelligence was a part of fictional stories, Science Fiction books, and even movies. It was tech-magic to the eyes. Today, as things are slowly moving towards reality, we can see that AI is still exciting, even though it's not as advanced as shown in the movies. Businesses are slowly investing in AI technology so that they can grow to be smarter and more efficient. Investments in AI as a technology haven't met the hype, but there is a positive sign of acceptance towards the smart technology in IoT.

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human-computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia.

Explaining recommendations enables users to understand whether recommended items are relevant to their needs and has been shown to increase their trust in the system. More generally, if designing explainable machine learning models is key to check the sanity and robustness of a decision process and improve their efficiency, it however remains a challenge for complex architectures, especially deep neural networks that are often deemed "black-box". In this paper, we propose a novel formulation of interpretable deep neural networks for the attribution task. Differently to popular post-hoc methods, our approach is interpretable by design. Using masked weights, hidden features can be deeply attributed, split into several input-restricted sub-networks and trained as a boosted mixture of experts. Experimental results on synthetic data and real-world recommendation tasks demonstrate that our method enables to build models achieving close predictive performances to their non-interpretable counterparts, while providing informative attribution interpretations.

DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions. The principal goal of this tool is to enable an efficient exploration of new musical ideas. We train a deep neural network classifier on audio data and show how it can be used as the core component of a system that generates drum loops based on few prior beliefs as to how these loops should be structured. We aim to build a system that can converge to meaningful results even with a limited number of interactions with the user. This property enables our method to be used from a cold start situation (no pre-existing dataset), or starting from a collection of audio samples provided by the user. In a proof of concept study with 25 participants, we empirically demonstrate that DeepDrummer is able to converge towards the preference of our subjects after a small number of interactions.

Nowcasting Earthquakes in Southern California with Machine Learning: Bursts, Swarms and Aftershocks May Be Related to Levels of Regional …

Amazon has published videos and supplementary materials from several of its internal Machine Learning University courses.